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The performance of traditional reconstruction-based robust adaptive beamformers may degrade when the array is not well calibrated. This performance degradation is mainly caused by the Capon spectrum estimator which can not estimate the spatial power spectrum accurately. In contrast to existing approaches, we propose two new reconstruction-based robust adaptive beamformers by using the accurate iterative adaptive approach (IAA) spectrum to combat the covariance matrix uncertainties and the steering vector mismatches. The first one employs the low-complexity IAA (IAA-LC) algorithm to obtain the interference power estimates and reconstruct the interference-plus-noise covariance matrix (INCM) with the computational complexity further reduced. The second one reconstructs the INCM by updating the power estimates corresponding to each interference steering vector with the use of knowledge-aided IAA (IAA-KA) algorithm. The desired signal steering vector is corrected by searching for the direction corresponding to the maximum power, which circumvents the use of optimization program. Simulation results show that our proposed beamformers outperform the others and can achieve the robust performance in the cases of array model mismatches. [ABSTRACT FROM AUTHOR]

Energy storage systems are one of the recent technologies utilized in order to achieve the stability and reliability of the power system with the integration of renewable energy sources. Energy storage systems can enhance system performance by means of the proper energy management. First, this paper presents a probabilistic modeling strategy for wind power and system demand. The modeling strategy utilizes Monte Carlo simulations to accurately consider the intermittent nature of wind generation and demand. Second, a linear programming optimization approach is performed to maximize system arbitrage benefits through optimizing operation of energy storage systems. Finally, two novel algorithms for optimal allocation of renewable energy resources and energy storage elements are proposed. The proposed algorithms are dependent on Big Bang Big Crunch optimization method and integrating the results of the modeling strategy with the optimization problem. The presented algorithms aim to minimize the net present value of energy loss cost in distribution systems. The proposed modeling strategy and optimization algorithms are implemented in MATLAB environment and tested on IEEE 33 bus system. Several case studies are done and the subsequent discussions show the effectiveness of the proposed algorithms. [ABSTRACT FROM AUTHOR]

This article offers some solutions used in the assembly of the computerized Uniform Certified Public Accountancy (CPA) licensing examination as practical alternatives for operational programs producing large numbers of forms. The Uniform CPA examination was offered as an adaptive multistage test (MST) beginning in April of 2004. Examples of automated assembly using mixed integer programming (MIP) solutions in Optimization Programming Language software (OPL Studio 3.6.1©, ILOG Inc., Gentilly, France) illustrate MST design features and implementation strategies that can be generalized to other testing programs. This article affords a compromise between the best possible combination of test content and sustainability and security over time by linear programming techniques that make use of mixed integer optimization algorithms. Relative optimization functions are formulated to ensure a variety of constraints are always met for "testlets" of differing difficulties to form panels for the MST design. Technical information is discussed to assist other practitioners and researchers with test assembly optimization problems. Some practical consequences from the selection of statistical targets on testlet exposure are illustrated. These results are described with respect to the fundamental principles of automated test assembly and continuous administration based on MST when testlet or item exposure and inventory rotation are critical concerns. [ABSTRACT FROM AUTHOR]